Fluid distribution systems are well known in the art. One example of a fluid distribution system is the system associated with heating, ventilating and air-conditioning (HVAC) distribution systems. HVAC distribution systems see widespread use in commercial applications, i.e., residential housing, apartment buildings, office buildings, etc. However, HVAC distribution systems also see widespread use in laboratory-type settings. In this implementation, the HVAC system is primarily intended to exhaust potentially noxious fumes, etc.
In a majority of HVAC distribution system implementations, the primary goal is to produce and distribute thermal energy in order to provide the cooling and heating needs of a particular installation. For purposes of analysis, the distribution system can be divided into two sub systems; global and local sub systems. The global sub system consists of a primary mover (i.e., a source) which might be a fan in an air distribution system or a pump in a water distribution system. Also included in the global sub system is the duct-work required to connect the global sub system to the local sub system. The local sub system primarily consists of dampers or valves.
Current control practice, in both commercial and laboratory HVAC distribution systems, separates the global sub system from the local sub system and accordingly treats the individual sub systems independent of one another. The result of this separation is (1) poor controllability, (2) energy waste throughout the system, and (3) costly commissioning (installation and maintenance) processing.
FIG. 1 generally depicts a prior art HVAC distribution system. As depicted in FIG. 1, a fan controller 103 controls the variable air volume by controlling the speed of a fan 106 so that a constant static pressure at an arbitrary duct location (for example, the location 114) is maintained. A damper 118 is controlled by a damper controller 124. The static pressure at the location 114 fluctuates as the flow requirement of the damper 118 varies. However, the fan controller 103 ignores the requirement of static pressure in the entire system so that the flow requirement of the damper 118 can be satisfied. In this scenario, the fan controller 103 attempts to maintain an arbitrarily selected pressure setpoint, which is often set based on a maximum operating design condition. During normal operating conditions, however, the system static pressure requirement is considerably lower than the design condition. This results in a considerable amount of energy waste since the fan continuously operates to satisfy the maximum static pressure setpoint. If, on the other hand, the setpoint is much lower than the system requirement, the system is incapable of satisfying the flow requirements, which results in an ineffective system. In addition, no scientific methods exist to determine the best (optimum) position of the static pressure sensor 112 within the duct 115. In other words, the positioning of the static pressure sensor 112 is more of an art than a science. Furthermore, the tuning of the VAV fan control can be time consuming and costly if the selected pressure setpoint and the position of the static pressure sensor 112 are chosen incorrectly.
In a high pressure system (when measured by the static pressure sensor 112), dampers or valves within the local sub system must be in an almost closed position to maintain their individual set points. This, however, leads to the generation of undue noise and pressure loss in the system. In addition, dampers or valves in the almost closed position exhibit highly non linear characteristics, making tuning and controllability of these elements a challenge.
In a control strategy which overcomes the inherent problems discussed above, identification of certain characteristics of the components (inter alia, dampers or fans) must be performed. Quick and accurate identification of these characteristics are crucial to the successful implementation of the control strategy. However, prior art techniques of identification of these characteristics have disadvantages themselves.
For example, statistical analysis has been used in HVAC distribution systems in an attempt to perform identification of characteristics of the components utilized within the HVAC system. The goal of the statistical analysis is to fit the past data with a "best-fit" equation in an attempt to predict a future control signal based on past history. In this scenario, an operator must input a priori knowledge of the equation type, or the code implemented in the statistical analysis must search for the "best-fit" equation exhaustively. The operator requirement is cumbersome and does not lend itself to real-time adaptive control of the components implemented in the HVAC distribution system. The code requirement is intensive and is likewise not well-suited for real-time adaptive control of components due to the time required to perform the exhaustive search for the "best-fit" equation constants.
Thus, a need exists for a method of component characteristic identification which does not require any user input and uses a simple code so that adaptive, real-time control of components implemented in a HVAC distribution system may be accomplished.